Maria A. Guarnera

Plasma microRNAs as potential biomarkers for non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death. Developing minimally invasive techniques that can diagnose NSCLC, particularly at an early stage, may improve its outcome. Using microarray platforms, we previously identified 12 microRNAs (miRNAs) the aberrant expressions of which in primary lung tumors are associated with early-stage NSCLC. Here, we extend our previous research by investigating whether the miRNAs could be used as potential plasma biomarkers for NSCLC. We initially validated expressions of the miRNAs in paired lung tumor tissues and plasma specimens from 28 stage I NSCLC patients by real-time quantitative reverse transcription PCR, and then evaluated diagnostic value of the plasma miRNAs in a cohort of 58 NSCLC patients and 29 healthy individuals. The altered miRNA expressions were reproducibly confirmed in the tumor tissues. The miRNAs were stably present and reliably measurable in plasma. Of the 12 miRNAs, five displayed significant concordance of the expression levels in plasma and the corresponding tumor tissues (all r>0.850, all P<0.05). A logistic regression model with the best prediction was defined on the basis of the four genes (miRNA-21, -126, -210, and 486-5p), yielding 86.22% sensitivity and 96.55% specificity in distinguishing NSCLC patients from the healthy controls. Furthermore, the panel of miRNAs produced 73.33% sensitivity and 96.55% specificity in identifying stage I NSCLC patients. In addition, the genes have higher sensitivity (91.67%) in diagnosis of lung adenocarcinomas compared with squamous cell carcinomas (82.35%) (P<0.05). Altered expressions of the miRNAs in plasma would provide potential blood-based biomarkers for NSCLC.

Diagnosis of lung cancer in individuals with solitary pulmonary nodules by plasma microRNA biomarkers

BackgroundMaking a definitive preoperative diagnosis of solitary pulmonary nodules (SPNs) found by CT has been a clinical challenge. We previously demonstrated that microRNAs (miRNAs) could be used as biomarkers for lung cancer diagnosis. Here we investigate whether plasma microRNAs are useful in identifying lung cancer among individuals with CT-detected SPNs.MethodsBy using quantitative reverse transcriptase PCR analysis, we first determine plasma expressions of five miRNAs in a training set of 32 patients with malignant SPNs, 33 subjects with benign SPNs, and 29 healthy smokers to define a panel of miRNAs that has high diagnostic efficiency for lung cancer. We then validate the miRNA panel in a testing set of 76 patients with malignant SPNs and 80 patients with benign SPNs.ResultsIn the training set, miR-21 and miR-210 display higher plasma expression levels, whereas miR-486-5p has lower expression level in patients with malignant SPNs, as compared to subjects with benign SPNs and healthy controls (all P ≤ 0.001). A logistic regression model with the best prediction was built on the basis of miR-21, miR-210, and miR-486-5p. The three miRNAs used in combination produced the area under receiver operating characteristic curve at 0.86 in distinguishing lung tumors from benign SPNs with 75.00% sensitivity and 84.95% specificity. Validation of the miRNA panel in the testing set confirms their diagnostic value that yields significant improvement over any single one.ConclusionsThe plasma miRNAs provide potential circulating biomarkers for noninvasively diagnosing lung cancer among individuals with SPNs, and could be further evaluated in clinical trials.

Small nucleolar RNA signatures as biomarkers for non-small-cell lung cancer

BackgroundNon-small-cell lung cancer (NSCLC) is the leading cause of cancer death. Early detection of NSCLC will improve its outcome. The current techniques for NSCLC early detection are either invasive or have low accuracy. Molecular analyses of clinical specimens present promising diagnostic approaches. Non-coding RNAs (ncRNAs) play an important role in tumorigenesis and could be developed as biomarkers for cancer. Here we aimed to develop small nucleolar RNAs (snoRNAs), a common class of ncRNAs, as biomarkers for NSCLC early detection. The study comprised three phases: (1) profiling snoRNA signatures in 22 NSCLC tissues and matched noncancerous lung tissues by GeneChip Array, (2) validating expressions of the signatures by RT-qPCR in the tissues, and (3) evaluating plasma expressions of the snoRNAs in 37 NSCLC patients, 26 patients with chronic obstructive pulmonary disease (COPD), and 22 healthy subjects.ResultsIn the surgical tissues, six snoRNAs were identified, which were overexpressed in all tumour tissues compared with their normal counterparts. The overexpressions of the genes in tumors were confirmed by RT-qPCR. The snoRNAs were stably present and reliably detectable in plasma. Of the six genes, three (SNORD33, SNORD66 and SNORD76) displayed higher plasma expressions in NSCLC patients compared with the cancer-free individuals (All < 0.01). The use of the three genes produced 81.1% sensitivity and 95.8% specificity in distinguishing NSCLC patients from both normal and COPD subjects. The plasma snoRNA expressions were not associated with stages and histological types of NSCLC (All > 0.05).ConclusionsThe identified snoRNAs provide potential markers for NSCLC early detection.

Quantification of Plasma miRNAs by Digital PCR for Cancer Diagnosis

Analysis of plasma microRNAs (miRNAs) by quantitative polymerase chain reaction (qPCR) provides a potential approach for cancer diagnosis. However, absolutely quantifying low abundant plasma miRNAs is challenging with qPCR. Digital PCR offers a unique means for assessment of nucleic acids presenting at low levels in plasma. This study aimed to evaluate the efficacy of digital PCR for quantification of plasma miRNAs and the potential utility of this technique for cancer diagnosis. We used digital PCR to quantify the copy number of plasma microRNA-21-5p (miR-21–5p) and microRNA-335–3p (miR-335–3p) in 36 lung cancer patients and 38 controls. Digital PCR showed a high degree of linearity and quantitative correlation with miRNAs in a dynamic range from 1 to 10,000 copies/μL of input, with high reproducibility. qPCR exhibited a dynamic range from 100 to 1X107 copies/μL of input. Digital PCR had a higher sensitivity to detect copy number of the miRNAs compared with qPCR. In plasma, digital PCR could detect copy number of both miR-21–5p and miR-335–3p, whereas qPCR was only able to assess miR-21–5p. Quantification of the plasma miRNAs by digital PCR provided 71.8% sensitivity and 80.6% specificity in distinguishing lung cancer patients from cancer-free subjects.

Analysis of MicroRNAs in Sputum to Improve Computed Tomography for Lung Cancer Diagnosis

Introduction: Computed tomography (CT) plays a central role in lung cancer diagnosis. However, CT has relatively low specificity, presenting a challenge in clinical settings. We previously identified 12 microRNAs (miRNAs) whose expressions in tumor tissues were associated with lung cancer. Methods: Using quantitative reverse transcriptase polymerase chain reaction, we aimed to identify miRNA biomarkers in sputum that could complement CT for diagnosis of lung cancer. Results: In a training set consisting of 66 lung cancer patients and 68 cancer-free smokers, 10 of the 12 miRNAs were differentially expressed between the cases and controls (p ⩽ 0.01). From the miRNAs, a logistic regression model was built on the basis of miR-31 and miR-210, both of which had the best prediction for lung cancer, producing an area under receiver operating characteristic curve of 0.83. Combined use of the two miRNAs yielded 65.2% sensitivity and 89.7% specificity, CT had 93.9% sensitivity and 83.8% specificity for lung cancer diagnosis. Notably, combined analysis of the miRNA biomarkers and CT produced a higher specificity than does CT used alone (91.2% versus 83.8%; p < 0.05). The diagnostic performance of the biomarkers was confirmed in a testing set comprising 64 lung cancer patients and 73 cancer-free smokers. Conclusion: The sputum miRNA biomarkers might be useful in improving CT for diagnosis of lung cancer, but further independent validation on an external and prospective cohort of patients is required.

Characterization of microRNA transcriptome in lung cancer by next-generation deep sequencing.

Non‐small cell lung cancer (NSCLC) is the leading cause of cancer death. Systematically characterizing miRNAs in NSCLC will help develop biomarkers for its diagnosis and subclassification, and identify therapeutic targets for the treatment. We used next‐generation deep sequencing to comprehensively characterize miRNA profiles in eight lung tumor tissues consisting of two major types of NSCLC, squamous cell carcinoma (SCC) and adenocarcinoma (AC). We used quantitative PCR (qPCR) to verify the findings in 40 pairs of stage I NSCLC tissues and the paired normal tissues, and 60 NSCLC tissues of different types and stages. We also investigated the function of identified miRNAs in lung tumorigenesis. Deep sequencing identified 896 known miRNAs and 14 novel miRNAs, of which, 24 miRNAs displayed dysregulation with fold change ≥4.5 in either stage I ACs or SCCs or both relative to normal tissues. qPCR validation showed that 14 of 24 miRNAs exhibited consistent changes with deep sequencing data. Seven miRNAs displayed distinctive expressions between SCC and AC, from which, a panel of four miRNAs (miRs‐944, 205‐3p, 135a‐5p, and 577) was identified that cold differentiate SCC from AC with 93.3% sensitivity and 86.7% specificity. Manipulation of miR‐944 expression in NSCLC cells affected cell growth, proliferation, and invasion by targeting a tumor suppressor, SOCS4. Evaluating miR‐944 in 52 formalin‐fixed paraffin‐embedded SCC tissues revealed that miR‐944 expression was associated with lymph node metastasis. This study presents the earliest use of deep sequencing for profiling miRNAs in lung tumor specimens. The identified miRNA signatures may provide biomarkers for early detection, subclassification, and predicting metastasis, and potential therapeutic targets of NSCLC.

Sputum microRNA biomarkers for identifying lung cancer in indeterminate solitary pulmonary nodules

Purpose: The early detection of lung cancer in heavy smokers by low-dose CT (LDCT) can reduce the mortality. However, LDCT screening increases the number of indeterminate solitary pulmonary nodules (SPN) in asymptomatic individuals, leading to overdiagnosis. Making a definitive preoperative diagnosis of malignant SPNs has been a clinical challenge. We have demonstrated that sputum miRNAs could provide potential biomarkers for lung cancer. Here, we aimed to develop sputum miRNA biomarkers for diagnosis of malignant SPNs. Experimental Design: Using quantitative RT-PCR, we evaluated expressions of 13 sputum miRNAs, previously identified sputum miRNA signatures of lung cancer, in a training set of 122 patients with either malignant (n = 60) or benign SPNs (n = 62) to define a panel of biomarkers. We then validated the biomarker panel in an internal testing set of 136 patients with either malignant (n = 67) or benign SPNs (n = 69), and an external testing cohort of 155 patients with either malignant (n = 76) or benign SPNs (n = 79). Results: In the training set, a panel of three miRNA biomarkers (miRs21, 31, and 210) was developed, producing 82.93% sensitivity and 87.84% specificity for identifying malignant SPNs. The sensitivity and specificity of the biomarkers in the two independent testing cohorts were 82.09% and 88.41%, 80.52% and 86.08%, respectively, confirming the diagnostic value. Conclusions: Sputum miRNA biomarkers may improve LDCT screening for lung cancer in heavy smokers by preoperatively diagnosing malignant SPNs. Nevertheless, a prospective study in a large population to validate the biomarkers is needed. Clin Cancer Res; 21(2); 484–9. ©2015 AACR.

Identification of ENO1 As a Potential Sputum Biomarker for Early-Stage Lung Cancer by Shotgun Proteomics

BACKGROUND Lung cancer is the leading cancer killer. Early detection will reduce the related deaths. The objective of this study was to identify potential biomarkers for early-stage lung cancer in sputum supernatant. MATERIALS AND METHODS Using shotgun proteomics, we detected changes in protein profiles that were associated with lung cancer by analyzing sputum supernatants from 6 patients with early-stage lung cancer and 5 cancer-free controls. Using western blotting, we validated the proteomic results in 22 lung cancer cases and 22 controls. Using enzyme-linked immunosorbent assay (ELISA), we evaluated the diagnostic performance of the biomarker candidates in an independent set of 35 cases and 36 controls. RESULTS Proteomics identified 8 biomarker candidates for lung cancer. Western blotting validation of the candidates showed that enolase 1 (ENO1) displayed a higher expression level in patients with cancer than in cancer-free individuals (P = .015). ELISA revealed that the assessment of ENO1 expression in sputum supernatant had 58.33% sensitivity and 80.00% specificity in distinguishing patients with stage I lung cancer from cancer-free individuals. CONCLUSION The analysis of protein biomarkers in sputum may provide a potential approach for the early detection of lung cancer. Future validation of all the candidates defined by shotgun proteomics in a large cohort study may help develop additional biomarkers that can be added to ENO1 to provide more diagnostic efficacy for lung cancer.

Analysis of small nucleolar RNAs in sputum for lung cancer diagnosis.

Molecular analysis of sputum presents a noninvasive approach for diagnosis of lung cancer. We have shown that dysregulation of small nucleolar RNAs (snoRNAs) plays a vital role in lung tumorigenesis. We have also identified six snoRNAs whose changes are associated with lung cancer. Here we investigated if analysis of the snoRNAs in sputum could provide a potential tool for diagnosis of lung cancer. Using qRT-PCR, we determined expressions of the six snoRNAs in sputum of a training set of 59 lung cancer patients and 61 cancer-free smokers to develop a biomarker panel, which was validated in a testing set of 67 lung cancer patients and 69 cancer-free smokers for the diagnostic performance. The snoRNAs were robustly measurable in sputum. In the training set, a panel of two snoRNA biomarkers (snoRD66 and snoRD78) was developed, producing 74.58% sensitivity and 83.61% specificity for identifying lung cancer. The snoRNA biomarkers had a significantly higher sensitivity (74.58%) compared with sputum cytology (45.76%) (P < 0.05). The changes of the snoRNAs were not associated with stage and histology of lung cancer (All P >0.05). The performance of the biomarker panel was confirmed in the testing cohort. We report for the first time that sputum snoRNA biomarkers might be useful to improve diagnosis of lung cancer.

Genome-wide small nucleolar RNA expression analysis of lung cancer by next-generation deep sequencing

Emerging evidence indicates that small nucleolar RNAs (snoRNAs), a class of small noncoding RNAs, may play important function in tumorigenesis. Nonsmall‐cell lung cancer (NSCLC) is the number one cancer killer for men and women. Systematically characterizing snoRNAs in NSCLC will develop biomarkers for its early detection and prognostication. We used next‐generation deep sequencing to comprehensively characterize snoRNA profiles in 12 NSCLC tissues. We used quantitative reverse transcription polymerase chain reaction (qRT‐PCR) to verify the findings in 40 surgical Stage I NSCLC specimens and 126 frozen NSCLC tissues of different stages. The 126 NSCLC tissues were divided into a training set and a testing set. Deep sequencing identified 458 snoRNAs, of which, 29 had a ≥3.0‐fold expression level change in Stage I NSCLC tissues versus normal tissues. qRT‐PCR analysis showed that 16 of 29 snoRNAs exhibited consistent changes with deep sequencing data. The 16 snoRNAs exhibited 0.75–0.94 area under receiver–operator characteristic curve values in distinguishing lung tumor from normal lung tissues (all ≤0.0001) with 70.0–95.0% sensitivity and 70.0–95.0% specificity. Six genes (snoRA47, snoRA68, snoRA78, snoRA21, snoRD28 and snoRD66) were identified whose expressions were associated with overall survival of the NSCLC patients. A prediction model consisting of three genes (snoRA47, snoRA68 and snoRA78) was developed in the training set of 77 cases, which could significantly predict overall survival of the NSCLC patients (p < 0.0001). The prognostic performance of the prediction model was confirmed in the testing set of 49 NSCLC patients. The identified snoRNA signatures may provide potential biomarkers for the early detection and prognostication of NSCLC.